Organizations involved in customer service activities often process large amounts of unstructured data to make decisions while interacting with a customer in real-time. For example, in the case of a customer service representative speaking on the telephone with a customer experiencing an issue with a product or service, appropriate solutions may include a combination of timeliness of response and accuracy in content.
Such unstructured data may include voluminous transaction records spanning decades, unstructured customer service data, or real-time transcripts of customer service interactions with scattered contextual indicators. To reasonably expect a customer service representative to effectively leverage such large data sets in real-time places an unreasonable burden on a customer service representative. However, failing to do so robs the customer service representative of vital context not readily apparent, and the wealth of knowledge gained throughout the history of an organization that would otherwise need to be distilled to briefing materials and expensively trained over time. Thus, organizations may value tools to rapidly process large data sets, to infer context, suggest lessons learned based upon transaction data, while learning through successive process iterations. Furthermore, appropriate application of such tools may provide a competitive advantage in a crowded and competitive customer service industry.
In an effort to automate and provide better predictability of customer service experiences, many organizations develop customer relationship management (CRM) software packages. Organizations that develop these software packages often develop custom solutions, at great expense, to best meet the needs of their customers in unique industries. Such tools while providing a great level of detail for the customer service representative, lack the flexibility to react to changing business conditions or fully exploit the underlying technology, driving additional cost into an already expensive solution.
Some organizations where able to make concessions on customized solutions turn to off-the-shelf or commercially available software solutions that reduce the overall cost of implementation. Such solutions may provide customer service representative prompting tools with question and answer formats that allow for consistency of customer experience, however, at the expense of a less personalized experience required in many industries. While more flexible than fully-custom solutions, the impersonal question-answer format of customer interaction may not improve without costly software revisions, rarely performed by original equipment manufacturers (OEMs) of off-the-shelf solutions.
The ability for a customer service experience to learn and improve over successive iterations remains paramount for organizations to offer discriminating customer service experiences. Often the burden of continual improvement falls to the customer service representative, as a human being able to adapt and learn to changing conditions more rapidly even within the confines of a rigid customer service software application. However, with the advent of outsourcing prevalent in the customer service industry, the customer service representative may lack much of the necessary context required to provide high levels of relevant customer service. This lack of context in an interconnected company is less an issue of distance and more an issue of data access and the ability to contextually process data to present relevant solutions in a timely manner.